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Cheolsoo Park - IEEE Xplore Author Profile

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This study introduces a dual-band absorptive bandpass filter that employs a dual-behavior matching section. The proposed design utilizes a filter methodology based on a low-pass filter prototype, enabling the development of higher-order distributed dual-band absorptive bandpass filters with the control of two center frequencies and bandwidths. The proposed filter requires fewer resonators than the...Show More
Spiking neural networks, known for mimicking the brain’s functionality resulting in efficient algorithms, are gaining attention across various problems and applications. However, their potential in regression tasks remains relatively unexplored. This study focuses on leveraging the spiking neural architecture in conjunction with Fourier analysis and support vector regression to estimate heart rate...Show More
Advances in mixed reality and blockchain technology in Metaverse are transforming the digital healthcare by enabling real-time health monitoring and remote medical treatments. This significantly enhances patient care and medical interactions, permitting virtual consultations without the need for physical hospital visits. This study utilized a dynamic vision sensor camera to track white blood cells...Show More
In Metaverse, where digital and physical realities converge, cameras serve as highly accessible sensors, crucial for non-invasive health monitoring. Remote photoplethysmography (rPPG) utilizes these devices to measure vital signs, a key component for enhancing user health and safety in virtual environments. However, the reliability of rPPG is compromised by user movemeats and variable lighting con...Show More
Motor imagery refers to the brain’s response during the mental simulation of physical activities, which can be detected through electroencephalogram (EEG) signals. However, EEG signals exhibit a low signal-to-noise ratio (SNR) due to various artifacts originating from other physiological sources. To enhance the classification performance of motor imagery tasks by increasing the SNR of EEG signals,...Show More
Healthcare Internet of Things (H-IoT) has been rigorously extending its applications to hospitals to deliver important medical data remotely and continuously with various types of smart medical sensors. Medical data of patients should be treated with different urgency levels. Specifically, critical medical data, such as data pertaining, to heart failure or fall risk must be transmitted first with ...Show More
This paper proposes a prediction model of car prices in the metaverse, using a machine-learning method. With the advent of the metaverse, virtual assets have gained significant value, and cars are no exceptions. In this context, predicting car prices in the metaverse is a great of interest for both buyers and sellers. To develop the prediction model, we use real data obtained from Kaflix company, ...Show More
The integration of IEEE802.11p and 5G New Radio for the backbone transmission of vehicle-to-everything (V2X) technology has served as a novel type of intelligent transportation system with an efficient low-cost and high-coverage area. This integrated scheme implements a V2X end-to-end data delivery system in a multilayer mechanism. However, the dynamic nature of vehicular networks leads to numerou...Show More
This letter develops an efficient self-optimizing algorithm for Bluetooth Low Energy (BLE) networks in industrial IoT (IIoT) applications. The ultra-dense nature of IIoT applications implies that BLE networks are associated with a high probability of packet collisions, increasing the data collection latency. Determining the optimal packet transmission interval that minimizes the data collection la...Show More
Various researches have been conducted to estimate remote photoplethysmography from video streams, which are mostly based on a convolutional neural network model. Although these yielded meaningful performance, it takes long to train the model and produce the inference during the testing process since there are numerous weights and they are less scalable to the multiple tasks. Therefore, we propose...Show More
Time series data could be incomplete due to a variety of reasons such as the errors in communications and sensor devices. This paper aims to restore the incomplete time-series medical data using the denoising diffusion probabilistic model (DDPM). A DDPM is applied to restore the missing values based on the model trained using the original data without loss. The proposed diffusion model-based signa...Show More
Blood oxygen speed (SpO2) is an indicator of the normal presence or absence of the respiratory function. This is attracting the attention of researchers since it could monitor the patient conditions of the chronic pulmonary diseases and covid-19. Covid-19 patients have the symptom of the significant SpO2 drop. This study tries to develop an early and easy checkup system of the continuous SpO2 usin...Show More
The ever-growing field of wireless communication requires efficient radio frequency transceivers with tunable absorptive bandstop filters that can prevent the interference of out-of-band and reflected signals. This paper presents 4-pole tunable absorptive bandstop filters using folded coupled-lines with an inductor. A 4-pole filter transfer function is achieved to obtain an absorptive bandstop fil...Show More
Work-related stress causes serious negative physiological and socioeconomic effects on employees. Detecting stress levels in a timely manner is important for appropriate stress management; therefore, this study proposes a deep learning (DL) approach that accurately detects work-related stress by using multimodal signals. We designed a protocol that simulates stressful situations and recruited 24 s...Show More
Sleep experts manually label sleep stages via polysomnography (PSG) to diagnose sleep disorders. However, this process is time-consuming, requires a lot of labor from sleep experts, and makes the participants uncomfortable with the attachment of multiple sensors. Thus, automatic sleep scoring methods are essential for practical sleep monitoring in our daily lives. In this study, we propose an auto...Show More
This paper demonstrates that an artificially generated normal electrocardiogram (ECG) using a generative adversarial network (GAN) model on the MIT-BIH arrhythmia dataset could hide the arrhythmia waveform while preserving the individual’s intrinsic characteristics. A seven-layer convolutional neural network (CNN) model was used to determine the presence of arrhythmia in normal ECG data generated ...Show More
An efficient channel assignment plays an important role in mitigating co-channel interference in ultra-dense wireless networks. A simple solution is to separate interfering network nodes into orthogonal channels to reduce the interference among them. However, determining the optimal channel assignment is considered to be a non-linear problem, which may also be associated with practical implementat...Show More
Cognitive radios require tunable band-switchable bandpass filters to respond to the dynamically changing operating frequencies in cognitive systems. This study presents a novel switchable dual-/single-band tunable bandpass filter using a single switchable J -inverter. The proposed filter configuration can be easily converted from a two-pole dual-passband mode to a four-pole single-passband mode us...Show More
Automatic detection of arrhythmia using electrocardiogram (ECG) signal is an important role in the early diagnosis of cardiovascular diseases. Most of the arrhythmia detection researches have focused on the analysis of 1D time-series ECG signals, where deep neural network architecture have been applied due to its reliable and high performance. However, the hyperparameters of the deep learning mode...Show More
Although heart rate is an important biomarker of the physical condition at active states of users, it is still difficult to be measured due to an ambient noise and movements. There have been several approaches proposed to obtain stable measurements in an active condition. However, these methods still need direct contact to users, and thus additional equipment to keep the contact are requested, res...Show More
Automatic nailfold capillary segmentation is a challenging task owing to noise and large variabilities in images caused by insufficient focusing and low visibility of the capillaries. This task can be useful to detect and estimate the severity of autoimmune diseases of connective tissues or learning the status of white blood cells based on the cells' blood flow on the nailfold capillary. Previous ...Show More
We report printed flexible optoelectronic sensors composed of red organic light-emitting diodes (OLEDs) and organic photodiodes (OPDs) for detection of various biological signals in a photoplethysmograph (PPG) device. Fabricated flexible OLEDs achieved maximum luminance >1000 cd/m2 at 9 V, with peak at 640 nm. Maximum flexible OPD photosensitivity for the poly(3-hexylthiophene-2, 5-diyl) and pheny...Show More
In this paper, we suggest an automated malware detection method using convolutional neural network (CNN) and other machine learning algorithms. Lately malware detection methods have been dependent on the selected packet field of applications such as the port number and protocols, which is why those methods are vulnerable to malwares with unpredictable port numbers and protocols. The proposed metho...Show More
The level of attention during a learning process affects the academic achievement of humans. Unlike traditional offline learning, the degree of attention to online learning is could be determined by the human's will to learn. In this study, we show the classification of the attention and non-attention to lecture videos, using electroencephalogram (EEG) recorded from subjects who were instructed to...Show More
Hand gesture recognition is one of the major research areas in the field of Human computer interaction (HCl). This paper proposes a deep reinforcement learning algorithm to recognize the human arm movement patterns using an IoT sensor device. Recent studies have explored supervised learning based methods, such as CNN and RNN to implement the HCl device. On the other hand, the deep reinforcement le...Show More